The potential public health impact of adolescent 4CMenB vaccination on Neisseria gonorrhoeae infection in England: a modelling study

Introduction Diagnoses of gonorrhoea in England rose by 26% between 2018 and 2019. Recent evidence that a vaccine against meningococcal B disease currently offered to infants in the UK (4CMenB) could additionally protect (with 31% efficacy) against gonorrhoea has led to renewed hope for a vaccine. A Phase 2 proof-of-concept trial of 4CMenB vaccination against gonorrhoea in adults is currently underway. Objectives To investigate the potential public health impact of adolescent gonorrhoea vaccination in England, considering different implementation strategies. Methods We developed a deterministic transmission-dynamic model of gonorrhoea infection among heterosexual 13–64-year-olds stratified by age, sex and sexual activity. We explored the impact of a National Immunisation Programme (NIP) among 14-year-olds for a vaccine with 31% efficacy, 6 years’ duration of protection, and 85% uptake. We also explored how impact might change for varying efficacy (20–50%) and uptake (75–95%), the addition of a catch-up programme, the use of boosters, and varying duration of protection. Results An NIP against gonorrhoea could lead to 50,000 (95% credible interval, CrI 31,000-80,000) and 849,000 (95%CrI 476,000-1,568,000) gonorrhoea infections being averted over 10 and 70 years, respectively, in England, for a vaccine with 31% efficacy and 85% uptake. This is equivalent to 25% (95%CrI 17–33%) of heterosexual infections being averted over 70 years. Vaccine impact is predicted to increase over time and be greatest among 13–18-year-olds (39% of infections 95%CrI 31–49% averted) over 70 years. Varying vaccine efficacy and duration of protection had a noticeable effect on impact. Catch-up and booster vaccination increased the short- and long-term impact, respectively. Conclusions A partially-effective vaccine against gonorrhoea infection, delivered to 14-year-olds alongside the MenACWY vaccine, could have an important population impact on gonorrhoea. Catch-up and booster vaccination could be considered alongside cohort vaccination to increase impact. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-14670-z.


Sexual behaviour
Individuals enter one of the four sexual activity classes on model entry, and can move between sexual activity classes as they age. The percentage of individuals in each of the four sexual activity classes by age, , , and the corresponding mean number of opposite-sex sexual partners per year for =2 and =3 ( =0 and =1 simply have 0 partners and 1 partner, respectively), , , were informed by data for England from the 3rd National Survey of Sexual Attitudes and Lifestyles (Natsal-3). We used data from 11,357 individuals aged 16-64 years surveyed between 2010-2012 to directly inform , and , for =4-6 (i.e., 17-64 year olds). We applied data for 16 year olds to the 15-16 year old age class ( =3). Model values for , were kept the same between 13 and 14 year olds, while model values for , were allowed to vary. We used data on age at first sex for 19-24 year olds to inform , for =0 for 13 and 14 year olds. The distribution of sexually-active 13 and 14 year olds in =1, =2 and =3 was then informed by data on the relative percentage of 15-16 year olds in each of these activity classes. Sexual behaviour parameters were assumed to be the same for women and men in order to balance the total number of partnerships.
Since 13 and 14 year olds were assumed to have the same values of , , there was no movement between sexual activity classes as individuals aged from 13 to 14 years. Data from Natsal 1-3(1) show a decrease in the percentage of individuals with no sexual partners with age across all ages, an increase in the percentage of individuals with one sexual partner with age across all ages, an increase in the percentage of individuals in =2 and =3 up to age 18 years, no clear pattern for the change in the assortative mixing within a class (4). The patterns of mixing by age and sexual activity class were defined by mixing matrices that balanced the "supply and demand" of partnerships. Individuals who were not currently sexually active (i.e., in class =0) did not contribute to sexual mixing by definition. Condom use and its effect on transmission were not explicitly modelled; however, the transmission probability per partnership is an average that will account for this. We also included a proportionate reduction in transmission among 25-64 year olds (calibrated), assuming a reduction in partner change rates in this age group that is not fully captured by differences in the number of partners by age (e.g., one partner per year could be one new partner each year or the same partner over many years).
Imported infections were assumed to be in the (male) =3 sexual activity class and distributed by vaccination status according to the percentage currently in each vaccination compartment.

Gonorrhoea vaccination
Vaccination was assumed to protect against acquisition of infection according to the vaccine efficacy. Vaccine efficacy can be defined by both degree and take. Vaccine take is the percentage of vaccinated individuals who develop an adequate immune response (i.e., are effectively vaccinated) that can protect them fully or partially against infection. The degree of protection is the proportionate reduction in the transmission probability per partnership to those individuals who respond to the vaccine (i.e., a proportionate reduction in infection acquisition among vaccinated individuals). In trials, it is generally not possible to differentiate between degree and take. Therefore, we did not distinguish between degree and take in the model, but instead, upon vaccination, moved all vaccinated individuals to the currently protected by vaccination model compartment. Vaccination was modelled using a pulse function, which moves all vaccinated individuals at the specified age from the never vaccinated compartment to the currently protected by vaccination compartment at the same time, rather than as an applied rate over the entire year. Within the currently protected by vaccination compartment an average reduction in the transmission probability per partnership was applied to all vaccinated individuals. This average reduction equated to the vaccine efficacy as measured in trials. It was assumed that vaccination affords no protection against duration of infection in breakthrough infections (which are possible in any scenario where vaccine efficacy is less than 100%), nor against infectivity of vaccinated individuals who acquire infection. Individuals can lose vaccine induced protection over time and as a result move into the waned vaccine protection compartment; the rate of movement assumes an exponential decline in protection based on the average duration of protection. In this compartment, individuals were assumed to have the same probability of gonorrhoea acquisition as those never vaccinated. Individuals remain in the waned vaccine protection for the remainder of their time in the model except for booster scenarios.
Booster vaccination was simulated by moving individuals out of the waned vaccine protection compartment into the currently protected by vaccination model compartment. In practice, individuals in the currently protected by vaccination model compartment would also receive the booster (as booster vaccination would be given to any individual previously vaccinated). In the model, such individuals simply remain in the currently protected model compartment. It was assumed that booster vaccination has no effect on the degree or duration of the existing protection of individuals in the currently protected model compartment, i.e., "boosted" individuals already protected by vaccination exit into the waned vaccine protection model compartment at the same rate as individuals who do not receive a booster and the vaccine efficacy does not change. Boosting only begins when those who were the first cohort to receive vaccination reach the age individuals are first eligible to be boosted (19 years; year 2023).
Similarly, the proportion of individuals currently infected with gonorrhoea at time is denoted by , , ( ). where _ , , is the net change due to ageing, denotes the rate of model entry, , denotes the percentage of individuals in each sexual activity class, , , denotes the force of (gonorrhoea) infection, denotes the rate of recovery from infection, denotes the cohort or catch-up vaccination uptake, , , represents imported infections (among bisexual men into =3 sexual activity class) and , , denotes the total number of individuals. where is the rate at which vaccine protection wanes and is the booster vaccination uptake. where _ , , is the net change due to ageing.

Force of gonorrhoea infection
The force of infection, , , , is defined as follows where is the transmission probability per partnership per year, is the average proportionate reduction in the transmission probability per partnership as a consequence of vaccination, , is the annual number of VI sexual partners (assumed to be the same for both women and men), and is the weighted prevalence of infection among partners of opposite sex ′ of age class and in sexual activity class ( =1, =2 and =3 only). Sexual mixing with opposite-sex partners by age and sexual activity class is defined by the mixing matrix , , , (which again is the same for women and men). This in turn is the product of the mixing matrix , which defines mixing by age class, and mixing matrix , which defines mixing by sexual activity class.
The mixing matrix , is given by where is the degree of assortative mixing for mixing by age class (2, 3) if 1 represents fully assortative mixing and 0 represents fully random (proportionate) mixing, and is the identity matrix such that = 1 for = and = 0 when ≠ . Defining the mixing matrix as above means that mixing takes into account differences in population numbers by age.
Similarly, the mixing matrix , is given by where is the degree of assortative mixing for mixing by sexual activity class (2,4).
For a list of parameter symbols and descriptions please refer to Table S1. For calibration and validation data please refer to Table S2.
The model was coded and analysed using R v.3.5.1, and the model ODEs were solved using deSolve (packages ode and default integrator lsoda). The time step used was one month. Ageing was done at the start of each calendar year. Vaccination (cohort, catch-up or booster) was done halfway through each calendar year. At all other timesteps waning vaccination protection occurred at a rate adjusted to account for the fact that waning did not occur when either ageing or vaccination occurred.

Model analysis
We modelled the following vaccination scenarios: Cohort adolescent vaccination of 14 year olds (vaccine uptake 85%) with higher baseline gonorrhoea incidence: Vaccine efficacy 31%; +26% incident gonorrhoea infections per year among heterosexual women and men used for fitting (resulting in an increase in overall incidence of ~22%) [S28] Cohort adolescent vaccination of 14 year olds (vaccine uptake 85%) with lower importation of infections: Vaccine efficacy 31%; -75% imported infections [S29]  UKHSA data tables on number of new diagnoses of gonorrhoea in women and men attending GUM/Level 3 and non-GUM/Level 2 services in England in 2018 (8). Data by single year of age for 13-24 year olds obtained from UKHSA and differ slightly from reported data. The number of diagnoses in 13 year olds and 14 year olds are estimates. Male diagnoses were adjusted by subtracting the estimated number of diagnoses among MSM. Female diagnoses were assumed to be for heterosexual women. To derive estimates of the total number of infections for calibrating the model reported diagnoses were adjusted using 1/the percentage of infections that are diagnosed. Percentage of infections that are diagnosed were based on diagnosis estimates of 89% of symptomatic infections, 40% of asymptomatic infections among women and 9% of asymptomatic infections among men, assuming 37.5% and 67.5% of infections in women and men, respectively, symptomatic(7, 10-13) giving an adjustment factor of 1.71 for women and 2.66 for heterosexual men, assuming that all infections MSM are diagnosed.
Assumed to be at equilibrium. Assumed to be at equilibrium. Age classes for data did not perfectly align with those in model.  Figure S1: Comparison of model baseline gonorrhoea incidence (annual cases per year) with infection data, for women and men, by age group Baseline gonorrhoea incidence (excluding imported infections) for the 100 best model fits is shown by the grey boxplots, with the median value for incidence represented by a black line. The red lines show point estimates of infection incidence for heterosexual women and men from data, adjusted for underreporting.